{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Otto商品分类——PCA\n",
    "训练集\n",
    "\n",
    "Otto数据集是著名电商Otto提供的一个多类商品分类问题，类别数=9. 每个样本有93维数值型特征（整数，可能表示某种事件发生的次数，已经进行过脱敏处理）。 \n",
    "竞赛官网：https://www.kaggle.com/c/otto-group-product-classification-challenge/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入必要的工具包\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>feat_1_tfidf</th>\n",
       "      <th>feat_2_tfidf</th>\n",
       "      <th>feat_3_tfidf</th>\n",
       "      <th>feat_4_tfidf</th>\n",
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       "      <th>feat_6_tfidf</th>\n",
       "      <th>feat_7_tfidf</th>\n",
       "      <th>feat_8_tfidf</th>\n",
       "      <th>feat_9_tfidf</th>\n",
       "      <th>...</th>\n",
       "      <th>feat_85_tfidf</th>\n",
       "      <th>feat_86_tfidf</th>\n",
       "      <th>feat_87_tfidf</th>\n",
       "      <th>feat_88_tfidf</th>\n",
       "      <th>feat_89_tfidf</th>\n",
       "      <th>feat_90_tfidf</th>\n",
       "      <th>feat_91_tfidf</th>\n",
       "      <th>feat_92_tfidf</th>\n",
       "      <th>feat_93_tfidf</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.081393</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075886</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Class_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.231403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Class_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.199730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Class_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.011987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011668</td>\n",
       "      <td>0.105971</td>\n",
       "      <td>0.021681</td>\n",
       "      <td>0.080435</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008244</td>\n",
       "      <td>0.022456</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Class_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.124622</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.145988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Class_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  feat_1_tfidf  feat_2_tfidf  feat_3_tfidf  feat_4_tfidf  feat_5_tfidf  \\\n",
       "0   1      0.081393           0.0           0.0      0.000000      0.000000   \n",
       "1   2      0.000000           0.0           0.0      0.000000      0.000000   \n",
       "2   3      0.000000           0.0           0.0      0.000000      0.000000   \n",
       "3   4      0.011987           0.0           0.0      0.011668      0.105971   \n",
       "4   5      0.000000           0.0           0.0      0.000000      0.000000   \n",
       "\n",
       "   feat_6_tfidf  feat_7_tfidf  feat_8_tfidf  feat_9_tfidf  ...  feat_85_tfidf  \\\n",
       "0      0.000000      0.000000      0.000000           0.0  ...       0.075886   \n",
       "1      0.000000      0.000000      0.231403           0.0  ...       0.000000   \n",
       "2      0.000000      0.000000      0.199730           0.0  ...       0.000000   \n",
       "3      0.021681      0.080435      0.000000           0.0  ...       0.000000   \n",
       "4      0.000000      0.000000      0.000000           0.0  ...       0.124622   \n",
       "\n",
       "   feat_86_tfidf  feat_87_tfidf  feat_88_tfidf  feat_89_tfidf  feat_90_tfidf  \\\n",
       "0       0.000000       0.000000            0.0            0.0       0.000000   \n",
       "1       0.000000       0.000000            0.0            0.0       0.000000   \n",
       "2       0.000000       0.000000            0.0            0.0       0.000000   \n",
       "3       0.008244       0.022456            0.0            0.0       0.000000   \n",
       "4       0.000000       0.000000            0.0            0.0       0.145988   \n",
       "\n",
       "   feat_91_tfidf  feat_92_tfidf  feat_93_tfidf   target  \n",
       "0            0.0            0.0            0.0  Class_1  \n",
       "1            0.0            0.0            0.0  Class_1  \n",
       "2            0.0            0.0            0.0  Class_1  \n",
       "3            0.0            0.0            0.0  Class_1  \n",
       "4            0.0            0.0            0.0  Class_1  \n",
       "\n",
       "[5 rows x 95 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取训练数据\n",
    "train = pd.read_csv(\"./Otto_FE_train_tfidf.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>11287</th>\n",
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       "      <td>...</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>10355</th>\n",
       "      <td>10356</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.201673</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>13438</th>\n",
       "      <td>13439</td>\n",
       "      <td>0.129754</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.089243</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.196017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  feat_1_tfidf  feat_2_tfidf  feat_3_tfidf  feat_4_tfidf  \\\n",
       "7897    7898      0.000000           0.0           0.0           0.0   \n",
       "11287  11288      0.000000           0.0           0.0           0.0   \n",
       "10355  10356      0.000000           0.0           0.0           0.0   \n",
       "13438  13439      0.129754           0.0           0.0           0.0   \n",
       "54129  54130      0.000000           0.0           0.0           0.0   \n",
       "\n",
       "       feat_5_tfidf  feat_6_tfidf  feat_7_tfidf  feat_8_tfidf  feat_9_tfidf  \\\n",
       "7897            0.0           0.0           0.0      0.000000           0.0   \n",
       "11287           0.0           0.0           0.0      0.000000           0.0   \n",
       "10355           0.0           0.0           0.0      0.201673           0.0   \n",
       "13438           0.0           0.0           0.0      0.000000           0.0   \n",
       "54129           0.0           0.0           0.0      0.100217           0.0   \n",
       "\n",
       "       ...  feat_84_tfidf  feat_85_tfidf  feat_86_tfidf  feat_87_tfidf  \\\n",
       "7897   ...            0.0            0.0       0.242627            0.0   \n",
       "11287  ...            0.0            0.0       0.067614            0.0   \n",
       "10355  ...            0.0            0.0       0.331142            0.0   \n",
       "13438  ...            0.0            0.0       0.089243            0.0   \n",
       "54129  ...            0.0            0.0       0.000000            0.0   \n",
       "\n",
       "       feat_88_tfidf  feat_89_tfidf  feat_90_tfidf  feat_91_tfidf  \\\n",
       "7897        0.266458            0.0       0.000000            0.0   \n",
       "11287       0.074256            0.0       0.000000            0.0   \n",
       "10355       0.000000            0.0       0.000000            0.0   \n",
       "13438       0.196017            0.0       0.000000            0.0   \n",
       "54129       0.000000            0.0       0.065327            0.0   \n",
       "\n",
       "       feat_92_tfidf  feat_93_tfidf  \n",
       "7897             0.0            0.0  \n",
       "11287            0.0            0.0  \n",
       "10355            0.0            0.0  \n",
       "13438            0.0            0.0  \n",
       "54129            0.0            0.0  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = train['target']   \n",
    "x_train = train.drop([\"target\"], axis=1)\n",
    "\n",
    "# 用train_test_split，从将数据集中随机抽取10000条记录来训练模型\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_abandon, Y_train, y_abandon = train_test_split(x_train, y_train, train_size = 10000,random_state = 0)\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_id = X_train['id']\n",
    "X_train = X_train.drop([\"id\"], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA降维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components = 0.85) #最后保留的PCA的维数可以解释的方差占总方差的85%\n",
    "pca.fit(X_train) #fit的目的是要找到pca的积和其他参数\n",
    "    \n",
    "# 在训练集和测试集降维 （fit好后，对训练数据和测试数据都可以直接调用transform得到结果）\n",
    "X_train_pca = pca.transform(X_train) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制PCA维的方差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#做完pca之后已经进行了降序排列\n",
    "plt.bar(range(len(pca.explained_variance_ratio_)), pca.explained_variance_ratio_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.10328174 0.07309376 0.05565485 0.05175723 0.04338137 0.03671733\n",
      " 0.03331822 0.0288889  0.02748467 0.02164704 0.02026721 0.01953563\n",
      " 0.01793453 0.01758126 0.01604417 0.01450248 0.01316403 0.01234489\n",
      " 0.01209442 0.01173033 0.0113773  0.01114325 0.01045222 0.01035377\n",
      " 0.01013807 0.00976079 0.00950668 0.00924167 0.0090827  0.0086904\n",
      " 0.00840557 0.00819665 0.00800708 0.00781366 0.0076949  0.00755774\n",
      " 0.00745103 0.00712087 0.00684454 0.00661642 0.00660277 0.00646681\n",
      " 0.00635684 0.0062452  0.0060737  0.00589186 0.00578647 0.00553691]\n",
      "48\n"
     ]
    }
   ],
   "source": [
    "print(pca.explained_variance_ratio_)\n",
    "print(pca.n_components_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 保存结果，PCA降维后的表示可作为特征提取的一部分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#保存PCA特征变换结果\n",
    "n_components = pca.n_components_\n",
    "\n",
    "#先生成特征名字，即csv文件的表头\n",
    "feat_names_pca = []\n",
    "for i in range(n_components):\n",
    "    feat_names_pca.append(\"pca_\" + str(i))\n",
    "    \n",
    "#把原来的id，y,加上pca的特征一起组成csv文件存起来\n",
    "y = pd.Series(data = Y_train, name = 'target')\n",
    "dataframe=pd.DataFrame(columns = feat_names_pca, data = X_train_pca)\n",
    "\n",
    "X_train_id.reset_index(drop=True, inplace=True)\n",
    "dataframe.reset_index(drop=True, inplace=True)\n",
    "y.reset_index(drop=True, inplace=True)\n",
    "\n",
    "train_pca = pd.concat([X_train_id,dataframe, y], axis = 1)\n",
    "train_pca.to_csv('./Otto_FE_train_tfidf_10000_PCA.csv',index=False,header=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 保存特征编码过程中用到的模型，用于后续对测试数据的特征编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(pca, open(\"pca_tfidf_10000.pkl\", 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 在PCA前两维上显示降维后的数据分布\n",
    "因为可视化通常只能显示二维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#变换后的数据，对应的标签（不同类别样本用不同颜色表示），标签对应的颜色，图的title\n",
    "def plot_embedding_PCA(data, targets, colors, title):\n",
    "    #x_min, x_max = np.min(data, 0), np.max(data, 0)\n",
    "    #data = (data - x_min) / (x_max - x_min)\n",
    "\n",
    "    fig = plt.figure(figsize = (8,8))\n",
    "    ax = fig.add_subplot(1,1,1) \n",
    "    \n",
    "    for target, color in zip(targets,colors):\n",
    "        indicesToKeep = data['target'] == target   #对每一类样本找出来\n",
    "        ax.scatter(data.loc[indicesToKeep, 'pca_0'] #用对应的颜色画出来\n",
    "               , data.loc[indicesToKeep, 'pca_1']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "    \n",
    "    ax.legend(targets)\n",
    "    ax.grid()\n",
    "    \n",
    "    ax.set_xlabel('Principal Component 1', fontsize = 15) #设置x,y坐标轴名字\n",
    "    ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "    ax.set_title(title, fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "targets = []\n",
    "colors = []\n",
    "#表示每一列的颜色和名字\n",
    "for i in range(9):\n",
    "    colors.append(plt.cm.Set1(i/ 10.))\n",
    "    targets.append(\"Class_\" + str(i+1))\n",
    "    \n",
    "plot_embedding_PCA(train_pca, targets, colors,\"PCA embedding of Otto Dataset tfidf 10000\")"
   ]
  }
 ],
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